Legal timeline analytics

ABSTRACT

Various of the disclosed embodiments concern systems and methods for applying legal analytics. In some embodiments, a legal analytics platform retrieves legal data from an electronic database, analyzes some or all of the legal data, and identifies interesting patterns and results of statistical analyses. In order to permit searching of the legal data, metadata elements or tags can be generated for legal entities and legal events. In some embodiments, the legal analytics platform identifies timestamps in the legal data and performs time-based statistical analysis. Results of the statistical analyses can be presented to a user via a graphical user interface (GUI), which may also allow the user to interact with the legal analytics platform and search one or more databases of legal data.

CLAIM OF PRIORITY

This application claims the benefit of U.S. Provisional PatentApplication No. 62/151,310, entitled “LEGAL ANALYTICS BASED ON PARTY,JUDGE, OR LAW FIRM,” filed Apr. 22, 2015, which is incorporated byreference herein in its entirety.

CROSS-REFERENCE TO RELATED APPLICATION(S)

The present application is related to concurrently filed applicationsU.S. patent application Ser. No. 14/823,461, titled “ LEGAL ANALYTICSBASED ON PARTY, JUDGE, OR LAW FIRM,” filed Aug. 11, 2015, U.S. patentapplication Ser. No. 14/823,496, titled “ANALYZING AND CHARACTERIZINGLEGAL CASE OUTCOMES,” filed Aug. 11, 2015, and U.S. patent applicationSer. No. 14/823,653 titled “MOTION MAPPING AND ANALYTICS,” filed Aug.11, 2015.

COPYRIGHT NOTICE

A portion of this patent document contains material that is subject tocopyright protection. To the extent required by law, the copyright ownerhas no objection to the facsimile reproduction of the document, as itappears in the U.S. Patent and Trademark Office patent file or records,but otherwise reserves all copyright rights whatsoever.

FIELD OF THE INVENTION

Various embodiments relate generally to computer applications. Morespecifically, various embodiments relate to legal analytics systems andmethods for discovering meaningful patterns in legal data.

BACKGROUND

Intellectual property has become increasingly more prominent as abusiness asset. For example, patent assets have received increased mediaattention as they have been the subject of business transactions, suchas patent auctions, and contested matters, such as patent litigation.

The United States has seen an explosion in patent litigation lawsuits inrecent years. For example, according to data aggregated by Lex Machina,Inc., of Menlo Park, Calif., in 2000 there were 2,281 patent lawsuitsfiled. By 2011, that number had climbed to 3,557. And, in 2013, a record6,082 patent lawsuits were filed. Recent trends in litigation have alsomotivated renewed interest in trademark, copyright, and antitrustlitigation.

Conventionally, attorneys, law students, legal professionals, etc.,required access to various information when doing legal research, suchas case opinions, statutes, and law review articles. Oftentimes, theinformation is stored in a variety of forms across a variety ofdifferent sources (e.g., hard copy materials, electronic databases) thatmakes research difficult and time-consuming. Access to more than onetype or source of information is often desirable, if not necessary,before determining what actions to take. Although online legal researchservices, such as Westlaw® and LexisNexis®, have significantly improvedaccessibility of legal documents and legal information, they do littleto make determining the proper course of action easier.

Online legal research services serve largely as electronic databases forlegal and public-records related information. As such, they providesignificant, but limited, value to those doing legal research. Forexample, an attorney that is researching strategies for a patentlitigation proceeding in the U.S. District Court for the North Districtof California may have access to information through services such asWestlaw®, LexisNexis®, Public Access to Court Electronic Records(PACER), Patent Application Information Retrieval (PAIR), ElectronicDocument Information System (EDIS), etc. Yet determining what strategiesare most likely to succeed in a particular jurisdiction, in front of aparticular judge, or against opposing counsel, still remains ad hoc andinstinctual.

SUMMARY

Systems and methods are described for acquiring, analyzing, anddiscovering meaningful patterns within legal data. In variousembodiments, a method includes accessing a source of information (e.g.,an electronic database) and obtaining legal data from the source. Thesource may be accessed over a network (e.g., Internet, a local areanetwork, a wide area network, a point-to-point dial-up connection) by alegal analytics system. Legal constructs in the form of metadata can berecognized by the system and mapped to legal entities, legal events,timestamps, etc. Pieces of metadata, also called “metadata elements,”may be associated with the legal data, thereby indicating the presenceof one or more legal entities, events, timestamps, etc. That is, thelegal data can be “tagged” using metadata. In some embodiments, some orall of the legal data is used to construct a database that is searchableby using the metadata. That is, a user (e.g., lawyer, law student) couldsearch the database for legal data associated with a particular legalentity or event.

In various embodiments, the method further includes generating agraphical user interface (GUI) that allows the user to specify searchparameters with which to search the database, present search results,and/or allow the user to modify the search parameters. The search resultcan include textual, tabular, or graphical summaries of the relevantlegal data. Some embodiments present the relevant legal data in morethan one way (e.g., tabular and graphical summary). The GUI can beconfigured to be presented by a web application or web-based portal, webbrowser, or a mobile application adapted for a cellular device, personaldigital assistant (PDA), tablet, personal computer, etc.

The method may also include identifying and normalizing inaccuracies(e.g., typos, misspellings) in the legal data. In some embodiments, themethod permits an administrator (e.g., individual, computing system) tomodify the legal data and/or input supplementary legal data. Thesupplementary legal data may be, for example, legal entities, outcomes,events, etc., that were not properly recognized by the legal analyticssystem.

In various embodiments, a method includes identifying timestamps withinthe legal data that identify the occurrence of a legal event and formingsubsets of timestamps grouped by legal event. A statistical analysis maybe performed on one or more subsets, thereby generating values (e.g.,median time, minimum time) that may be useful in determining futurebehavior. In some embodiments, the timestamps are also associated withone or more pieces of metadata, which allows the statistical analysis tobe performed on a subset of timestamps for a particular legal entity,event, etc.

In various embodiments, a method includes identifying electronic legaldocuments within the legal data, performing word recognition on thedocuments, and associating each document with a legal entity, event,etc., based on any recognized words. Sets of related documents may beconstructed in some embodiments.

The legal analytics system described herein can include, or be connectedto, a storage that includes some or all of the legal data, supplementarylegal data, and tags. In some embodiments, the storage also includessearch results or analytics reports previously generated for aparticular user, a particular legal entity, etc. The legal analyticssystem may include Natural Language Processing (NLP) and/or machinelearning modules that can be used to improve the system's accuracy inidentifying relevant data and discovering patterns.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features, and characteristics will become moreapparent to those skilled in the art from a study of the followingDetailed Description in conjunction with the appended claims anddrawings, all of which form a part of this specification. While theaccompanying drawings include illustrations of various embodiments, thedrawings are not intended to limit the claimed subject matter.

FIG. 1 is a generalized block diagram depicting certain components in alegal analytics system in accordance with various embodiments.

FIG. 2 is a generalized block diagram depicting components of oneexample of a legal analytics system.

FIG. 3 is a block diagram illustrating a legal analytics system,including a legal analytics platform on which at least some operationsdescribed herein can be implemented according to various embodiments.

FIG. 4 is a block diagram with exemplary components of a legal analyticsplatform for acquiring and analyzing legal data.

FIG. 5 is a flow diagram illustrating an overview of a process forretrieving, preparing, and delivering legal data according to variousembodiments.

FIG. 6 is a flow diagram depicting general steps in a legal analyticsprocess as may occur in some embodiments.

FIG. 7 is a flow diagram depicting various steps in a process forgenerating and implementing a GUI according to various embodiments.

FIG. 8 is a flow diagram depicting various steps in an entitynormalization and curation process as may occur in some embodiments.

FIGS. 9, 10, and 11 are screenshots of GUIs in accordance with variousembodiments.

FIG. 12 is a flow diagram depicting general steps in a legal analyticsprocess as may occur in some embodiments.

FIGS. 13, 14, and 15 are screenshots of GUIs in accordance with variousembodiments.

FIG. 16 is a flow diagram depicting general steps in a legal analyticsprocess as may occur in some embodiments.

FIG. 17 is a screenshot of a GUI displaying a summary of a statisticalanalysis of groups of timestamps as may occur in some embodiments.

FIG. 18 is a flow diagram illustrating a process for retrieving,preparing, and delivering legal data according to various embodiments.

FIG. 19 is a screenshot of a GUI presenting legal documents as may occurin some embodiments.

FIG. 20 is a screenshot of a GUI presenting a set of legal documents asmay occur in some embodiments.

FIG. 21 is a block diagram illustrating an example of a computing systemin which at least some operations described herein can be implemented.

The figures depict various embodiments described throughout the DetailedDescription for purposes of illustration only. While specificembodiments have been shown by way of example in the drawings and aredescribed in detail below, the invention is amenable to variousmodifications and alternative forms. The intention, however, is not tolimit the invention to the particular embodiments described.Accordingly, the claimed subject matter is intended to cover allmodifications, equivalents, and alternatives falling within the scope ofthe invention as defined by the appended claims.

DETAILED DESCRIPTION

Various embodiments are described herein that relate to systems andmethods for applying legal analytics. More specifically, variousembodiments relate to electronic systems and methods for acquiring legaldata and discovering meaningful patterns in the legal data.

As will be described below, legal data can be collected, or “mined,”from one or more sources of legal information. The legal data caninclude structured elements pulled directly from the source andunstructured elements that have been extracted from the legal data. Forexample, the legal data may include millions of pages of electroniclegal information. A legal analytics platform can process and analyzethe legal data, thereby revealing insights and patterns that would havebeen difficult or impossible to discover using conventional researchtechniques. The insights and patterns can be exploited by a user (e.g.,lawyer, legal practitioner, law student) to improve the likelihood ofsuccess in the practice of law. The techniques introduced herein can beembodied as special-purpose hardware (e.g., circuitry), as programmablecircuitry appropriately programmed with software and/or firmware, or asa combination of special-purpose and programmable circuitry. Hence,embodiments may include a machine-readable medium having stored thereoninstructions that may be used to program a computer (or anotherelectronic device) to perform a process. The machine-readable medium mayinclude, but is not limited to, floppy diskettes, optical disks, compactdisk read-only memories (CD-ROMs), magneto-optical disks, read-onlymemories (ROMs), random access memories (RAMs), erasable programmableread-only memories (EEPROMs), magnetic or optical cards, flash memory,or another type of media/machine-readable medium suitable for storingelectronic instructions.

Terminology

Brief definitions of terms, abbreviations, and phrases used throughoutthis application are given below.

Reference in this specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the disclosure. The appearances of the phrase “in one embodiment” invarious places in the specification are not necessarily all referring tothe same embodiment, nor are separate or alternative embodimentsmutually exclusive of other embodiments. Moreover, various features aredescribed that may be exhibited by some embodiments and not by others.Similarly, various requirements are described that may be requirementsfor some embodiments but not others.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” or any variant thereof, means any connection or coupling,either direct or indirect, between two or more elements; the coupling orconnection between the elements can be physical, logical, or acombination thereof. For example, two devices may be coupled directly,or via one or more intermediary channels or devices. As another example,devices may be coupled in such a way that information can be passedthere between, while not sharing any physical connection with oneanother. The words “associate with,” meanwhile, means connecting orrelating objects, items, etc. For example, a piece of metadata may beassociated with a particular legal entity. Additionally, the words“herein,” “above,” “below,” and words of similar import, when used inthis application, shall refer to this application as a whole and not toany particular portions of this application. Where the context permits,words in the Detailed Description using the singular or plural numbermay also include the plural or singular number respectively. The word“or,” in reference to a list of two or more items, covers all of thefollowing interpretations of the word: any of the items in the list, allof the items in the list, and any combination of the items in the list.

If the specification states a component or feature “may,” “can,”“could,” or “might” be included or have a characteristic, thatparticular component or feature is not required to be included or havethe characteristic.

The term “module” refers broadly to software, hardware, or firmware (orany combination thereof) components. Modules are typically functionalcomponents that can generate useful data or another output usingspecified input(s). A module may or may not be self-contained. Anapplication program (also called an “application”) may include one ormore modules, or a module may include one or more application programs.“Metadata” provides information about other data and may be derived orobtained from the other data. Metadata is often composed of variousindividual pieces of metadata, also called “metadata elements.”

The terminology used in the Detailed Description is intended to beinterpreted in its broadest reasonable manner, even though it is beingused in conjunction with certain examples. The terms used in thisspecification generally have their ordinary meanings in the art, withinthe context of the disclosure, and in the specific context where eachterm is used. For convenience, certain terms may be highlighted, forexample using capitalization, italics, and/or quotation marks. The useof highlighting has no influence on the scope and meaning of a term; thescope and meaning of a term is the same, in the same context, whether ornot it is highlighted. It will be appreciated that the same element canbe described in more than one way.

Consequently, alternative language and synonyms may be used for any oneor more of the terms discussed herein, and special significance is notto be placed upon whether or not a term is elaborated or discussedherein. Synonyms for certain terms are provided. A recital of one ormore synonyms does not exclude the use of other synonyms. The use ofexamples anywhere in this specification including examples of any termsdiscussed herein is illustrative only, and is not intended to furtherlimit the scope and meaning of the disclosure or of any exemplifiedterm. Likewise, the disclosure is not limited to various embodimentsgiven in this specification.

System Topology Overview

FIG. 1 is a generalized block diagram depicting certain components in alegal analytics system 100 in accordance with various embodiments. Alegal analytics platform 102 can access and retrieve legal data from oneor more sources of legal information 106 a-n. The legal data, or someportion thereof, can be stored in a storage 104. In some embodiments,the legal analytics platform 102 accesses the source(s) of legalinformation 106 a-n over a network 114 (e.g., Internet, a local areanetwork, a wide area network, a point-to-point dial-up connection). Thesource(s) of legal information 106 a-n can be, for example, PublicAccess to Court Electronic Records (PACER), Patent ApplicationInformation Retrieval (PAIR), or Electronic Document Information System(EDIS). Other network-accessible databases, such as RECAP, LexisNexis®,Westlaw®, Bloomberg Law®, and HeinOnline®, may also be accessed incertain embodiments. In some embodiments, the legal analytics platform102 retrieves legal data from more than one source. For example, theplatform 102 can access PACER to obtain legal documents and docketinformation for a district court case and PAIR to obtain information onany patents asserted in the case. Data may also be retrieved from otherresources 107, such as news feeds, social media, conventional searchengines, etc. The data can supplement any data retrieved from thesource(s) of legal information 106 a-c and be used to acknowledge recentevents associated with a particular law firm, lawyer, judge, patent,etc. A user might review the recent events (e.g., related patentrecently ruled invalid) to identify an appropriate next step in an openmatter.

A user, such as users 112 a-c, may interact with the legal analyticsplatform 102 via a GUI 108 displayed on one or more electronic devices110 a-c. The electronic devices 110 a-c may be, for example, mobilephones, PDAs, tablets (e.g., iPad®), personal computers, or wearabledevices (e.g., watches). Electronic devices 110 a-c can present a GUI108 for receiving user inputs, displaying results of statisticalanalyses, etc. In some embodiments, the electronic devices 110 a-ccommunicate with the legal analytics platform 102 over a network 116(e.g., Internet, a local area network, a wide area network, apoint-to-point dial-up connection). Network 116 can be the same as, ordifferent than, network 114.

In some embodiments, the legal analytics platform 102 is stored locally(e.g., as a set of machine-readable software instructions) on anelectronic device, such as electronic devices 110 a-c. Morespecifically, software for applying legal analytics can be installed andrun on a user-controlled device. In such embodiments, the user may beable to specify how often data is retrieved from the source(s) of legalinformation 106 a-n, how often legal analytics are applied, etc.

FIG. 2 is a generalized block diagram depicting certain components ofone example of a legal analytics system 200. As described above, thelegal analytics platform 202 can be configured to access and retrievelegal data from one or more sources of legal information 206 a-c. Forexample, legal analytics platform 202 of FIG. 2 is configured to accessPACER, PAIR, and EDIS. Any legal data retrieved from the sources 206 a-ccan be stored in a storage 204. In some embodiments, the storage 204 islocal with respect to the legal analytics platform 202 (e.g., legalanalytics platform 202 includes one or more storage modules). In otherembodiments, the legal analytics platform 202 is communicatively coupledto a remote storage 210.

After retrieving the legal data, the legal analytics platform 202 cananalyze some or all of the legal data. The analysis may lead to thediscovery of interesting trends, tendencies, and patterns within thelegal data that can be used to improve legal decision-making. As will bedescribed below, tendencies and patterns could be identified forparticular legal entities, legal events, and derived calculations usingsome combination of legal entities, legal events, or both. For example,a user may wish to search for judges by their motion grant rates. Insome embodiments, the legal analytics platform 202 generates a GUI 208that allows a user to interact with the platform 202. The GUI 208 canallow the user to specify search parameters with which to search thestorage 204, present search results, display textual or graphicalsummaries of the legal data, etc. The GUI could be presented to the userin many ways, including a web application or web-based portal, a webbrowser, a mobile application adapted for a cellular device, PDA,tablet, personal computer, etc. Similarly, the search results and/orsummaries could be presented through email, short message service (SMS),multimedia messaging service (MMS), etc.

FIG. 3 is a block diagram illustrating a legal analytics system 300,including a legal analytics platform 302 on which at least someoperations described herein can be implemented according to variousembodiments. The legal analytics platform 302 can include a data crawler304, an analytics engine 306, a normalization module 308, an entitycoding module 310, an event coding module 312, a key milestone module314, and/or a motion mapping module 316.

The data crawler 304 can be configured to access and retrieve legal datafrom one or more sources of legal information 318 a-n. The legal datamay include case and docket information, legal documents, party andparticipant (e.g., judge, attorney) information, dates of case events,judgments, case statuses, etc. After retrieving the legal data, ananalytics engine 306 can process and analyze some or all of the legaldata. More specifically, the analytics engine 306 can recognize legalconstructs within the legal data that map to legal entities, legalevents, or derived calculations using some combination thereof. In someembodiments, the legal data is converted into a format that is readableand searchable by the analytics engine 304. Conversion may be necessarywhen legal data is retrieved from sources that store legal data indifferent formats (e.g., HTML, PDF). The analytics engine 306 canconstruct one or more databases 320 a-n in which some or all of thelegal data is stored. For example, a database may include any legal dataretrieved from a single source of legal information. As another example,a database may include all legal data of a certain type (e.g.,chronology/timing information).

A normalization module 308 can be used to correct mistakes in the legaldata. As will be discussed in more detail below with respect to FIGS. 4and 8, the normalization module 308 can resolve typos (e.g., no spaces),misspellings, and/or references to an alternative name (e.g., prior lawfirm name), which are collectively referred to as “variants.” Thenormalization module 308 ensures that legal data (e.g., documents,outcomes) is attributed to the proper legal entity rather than avariant. Consequently, when a user conducts a search for a particularlegal entity, the user will receive all relevant legal data includingany legal data originally associated with a variant of the particularlegal entity.

An entity coding module 310 can generate metadata elements for legalentities identified within the legal data, identify and isolate portionsof legal data that include at least one legal entity, and associate eachportion with the appropriate metadata element(s). The term “legalentity” can refer to a judge, a jurisdiction, a lawyer, a law firm, aparty to a lawsuit, a patent, a trademark, a copyright, etc. Similarly,an event coding module 312 can generate metadata elements for legalevents, identify and isolate portions of legal data that include atleast one legal event, and associate each portion with the appropriatelegal event(s). The term “legal event” refers to any action oroccurrence that occurs before, during, or after a case. For example, alegal event may be commencement of a trial, decision of an issue byjudicial order, termination of a case, or commencement of a claimconstruction hearing. As another example, a legal event may be a “legaloutcome,” such as a case resolution (e.g., summary judgment, trial,dismissal), patent finding (e.g., infringement, invalidity,unenforceability), amount of damages, or damages type (e.g., reasonableroyalties, lost profit, attorneys' fees). Consequently, the entitycoding module 310 and event coding module 312 are able to tag portionsof legal data, thereby signaling which legal entities and/or legalevents (“metadata”) are present in each portion.

A key milestone module 314 can identify timestamps in the legal datacorresponding to the occurrence of a legal event. The timestamps may beextracted from, for example, docket information and case file documents.The key milestone module 314 can also generate metadata elements forlegal events and tag each timestamp with the appropriate legal event(s).The key milestone module 314 is able to associate timestamps with aparticular legal event. A “timestamp” is any sequence of characters orencoded information that identifies when a certain legal event occurred.For example, the legal data may include dates and times that can be usedto form chronological chains of legal events. As discussed below withrespect to FIGS. 18-20, a motion mapping module 316 can construct setsof electronic documents that include a motion and any subsequently-fileddocuments related to the motion. Each set of electronic documentsconstructed by the motion mapping module 316 represents a motion chain.In some embodiments, the motion mapping module 316 also generates anoutcome summary that indicates whether the motion was granted or denied.

FIG. 4 is a block diagram with exemplary components of a legal analyticsplatform 400 for acquiring and analyzing legal data. According to theembodiment shown in FIG. 4, the legal analytics platform 400 can includea data crawler 402, an analytics engine 404, a normalization module 416,an entity coding module 420, an event coding module 424, a key milestonemodule 428, a case list analytics module 430, a motion mapping module432, and a storage 434, any of which may include sub-components and/orsub-modules. Other embodiments of the legal analytics platform 400 mayinclude some, all, or none of these modules and components along withother modules, applications, and/or components. Still yet, someembodiments may incorporate two or more of these modules into a singlemodule and/or associate a portion of the functionality of one or more ofthese modules with a different module.

As described above, a data crawler 402 can be configured to access andretrieve legal data from a source of legal information. The analyticsengine 404 may include some combination of one or more processors 406, acommunication module 408, a GUI module 410, a learning module 412, and aNatural Language Processing (NLP) module 414. The processor(s) 406 canrun one or more applications or modules from instructions stored instorage 434, which can be any device or mechanism used for storinginformation. Communication module 408 may manage communications betweencomponents and/or other systems. For example, the communication module408 may be used to receive legal data from a source of legalinformation, transmit information (e.g., results of statisticalanalysis) to an electronic device, etc. The legal data received by thecommunication module 408 can be stored in storage 434, in one or moreparticular modules (e.g., storage modules 434 a-n), in a remote storagecommunicatively coupled to the legal analytics platform 400, or in somecombination thereof.

A GUI module 410 can generate a GUI that allows a user (e.g., lawyer,legal professional, law student) to interact with the legal analyticsplatform 400. In some embodiments, the GUI is presented to the user by aweb application or web-based portal, a web browser, a mobile applicationadapted for a cellular device, PDA, tablet, personal computer, etc.

Various embodiments can also employ a learning module 412 and/or an NLPmodule 414 that clean, code, and tag the legal data. The learning module412 and/or NLP module 414 may be able to identify legal entities, legalevents, timestamps, etc. The NLP module 414 can employ one or more wordrecognition processes to determine what words are present in the legaldata, while the learning module 412 can add, modify, delete, etc.,features from a ground truth value/set based on legal data that haspreviously been analyzed by the analytics engine 404. In someembodiments, the learning module 412 modifies how legal data isprocessed and analyzed by the analytics engine 404 based on what searchresults and statistical analyses the user has identified as important oruseful.

As described above, a normalization module 416 can resolve mistakes inthe legal data by correctly associating legal data (e.g., documents,outcomes) with the proper legal entity rather than any variants. In someembodiments, the normalization module 416 includes one or moresub-modules for normalization of a particular type of legal entity. Forexample, normalization module 400 includes a judge normalizer 418 a,jurisdiction (JDX) normalizer 418 b, lawyer normalizer 418 c, law firmnormalizer 418 d, and party normalizer 418 e. Various embodiments mayinclude some, all, or none of these sub-modules and may includeadditional sub-modules for other entities.

The entity coding module 420 can identify portions of legal data thatinclude one or more legal entities and tag the portions to signal whichlegal entities are present. In some embodiments, the entity codingmodule 420 includes an asserted intellectual property (IP) module 422that is able to identify references to intellectual property (e.g.,patents, trademarks, copyrights) within the legal data. The entitycoding module 420 can identify portions of legal data in whichintellectual property is referenced. More specifically, the asserted IPmodule 422 can generate metadata elements for each asserted piece ofintellectual property, identify portions of legal data in whichintellectual property is introduced or described, and associate theportions with the appropriate metadata element. For example, a caseopinion that describes two patents can be associated with two distinctmetadata elements. These metadata elements, as well as others,constitute the metadata used to describe the legal data. Markers may beused to indicate where each of the patents are mentioned within the caseopinion. The asserted IP module 422 can be configured to identifypatents, trademarks, copyrights, or any combination thereof.

The event coding module 424 can tag portions of legal data to signalwhat legal events, if any, are present within the legal data. Forexample, the event coding module 424 may be able to determine a patenthas been ruled invalid based on the recognized content of a caseopinion. In some embodiments, the event coding module 424 includes oneor more sub-modules configured to identify particular legal outcomes(e.g., DJ Tagging Module 426 to identify declaratory judgments). Thelegal analytics platform 400 can also include a key milestone module 428and/or a motion mapping module 432, as described above with respect toFIG. 3.

The case list analytics module 430 allows the user to select cases usingspecific criteria, such as case type, date range, jurisdiction, judge,case resolution, damages, and patent finding. A case list analyticsmodule 430 can collect legal data (e.g., from storage 434) associatedwith one or more cases specified by a user and identify meaningfulstatistics and patterns within the one or more cases. The analyticsengine 404 can then execute a variety of statistical analysis functionsto identify interesting information and trends in the cases. The caselist analytics module 430 and analytics engine 404 allow the user toreceive information without having to analyze each case within a caselist individually.

Legal Analytics Based on Legal Entity

FIG. 5 is a flow diagram illustrating an overview of a process 500 forretrieving, preparing, and delivering legal data according to variousembodiments. At block 506, a legal analytics platform retrieves (e.g.,using data crawler 402 of FIG. 4) legal data from one or more datasources 502. The data source(s) 502 may be electronic databases, such asPACER, PAIR, and EDIS. In some embodiments, the legal analytics platformretrieves structured elements 504 a pulled directly from the source(e.g., docket information, dates) and extracts unstructured elements 504b that are embedded within the legal data (e.g., electronic documents).

As will be discussed below with respect to FIG. 9, normalizing isgenerally a multi-step process. At block 508, the legal analyticsplatform can receive and store an authoritative entity name that isknown to be correct. The authoritative entity name may be provided by anadministrator (e.g., individual, computing system). While authoritativenames may improve the effectiveness of normalization, they are notnecessarily required. At block 510, the legal analytics platformidentifies variants of an entity name. The variants generally includetypos (e.g., no spaces), misspellings, or references to an alternativename (e.g., prior law firm name). In some instances the authoritativeentity name is used to identify variants, while in other instances thelegal analytics platform combines similar entities (i.e., names) into asingle category. At block 512, the structured elements 504 a and/orunstructured elements 504 b can be normalized, if necessary, to correctmistakes.

At block 514, the legal analytics platform generates metadata thatidentifies the presence of various legal entities. For example, piecesor elements of metadata may correspond to individual judges,jurisdictions, lawyers, law firms, parties to various lawsuits, patents,trademarks, copyrights, etc., identified in the legal data. At block516, the legal analytics platform associates portions of the legal datawith the metadata elements to signal what legal entities are present. Aportion of legal data could be associated with multiple legal entities.For example, an electronic legal document may be associated with aparticular judge and a particular law firm. Consequently, a user couldspecify a combination of legal entities when searching the legal data orfiltering search results.

At block 518, the legal analytics platform generates a GUI that can bepresented to the user. At block 520, the user is able to search thelegal data by specifying search parameters that include one or moremetadata elements (e.g., specifying one or more legal entities). Forexample, the user can search for legal data associated with a particularjudge, jurisdiction, or law firm. The search can be tailored by the userto identify particularly relevant insights. At block 522, the legalanalytics platform presents a search result to the user via the GUI. Thesearch result can include legal data, selectable hyperlinks to the legaldata, a textual summary, a graphical summary (e.g., a chart), etc. Theuser may elect to filter the search results by adding and/or removingmetadata elements from the search parameters.

In some embodiments, the process 500, or a subset of steps (e.g.,retrieving data 506), is periodic in nature. For example, the legalanalytics platform can be configured to retrieve data from the datasource(s) 502 hourly, daily, etc. Similarly, the legal analyticsplatform can be configured to apply legal analytics hourly, daily, etc.,or whenever a user inputs a request. In some embodiments, the legalanalytics platform retrieves data from distinct sources on distinctschedules. That is, the platform may retrieve data from a first datasource hourly and retrieve data from a second data source daily.

FIG. 6 is a flow diagram depicting general steps in a legal analyticsprocess 600 as may occur in some embodiments. At block 602, a legalanalytics platform accesses one or more sources of legal information(e.g., sources 106 a-n of FIG. 1). The sources of legal information canbe any network-accessible legal database, such as PACER, PAIR, EDIS,RECAP, LexisNexis®, Westlaw®, Bloomberg Law®, HeinOnline®, etc. At block604, the legal analytics platform retrieves legal data from at least oneof the sources of legal information.

In some embodiments, the legal analytics platform is configured toidentify inaccuracies in the legal data and, if necessary, normalize theinaccuracies as shown at block 606. The normalization process isdescribed below with respect to FIG. 8. In some embodiments, the legalanalytics platform allows an administrator to modify any legal datapreviously retrieved from source(s) of legal information and inputsupplementary legal data. For example, an administrator can manuallymodify aspects of the legal data by changing text that was incorrectlyrecognized during an OCR/word recognition process and can manually inputsupplementary legal data by adding a legal outcome for which no metadataelement has been generated.

At block 610, the legal analytics platform can generate metadataelements that represent various legal entities. The metadata elements,which collectively form metadata, support entity-based searching.However, as will be discussed below, metadata elements may also begenerated for other legal constructs, such as legal events. The metadataelements generated at block 610 in the present embodiment representdistinct legal entities. For example, each judge at the U.S. DistrictCourt for the Northern District of California (N.D.Cal.) can beassociated with an identifying metadata element that distinguishes himor her from all other judges. As another example, an identifyingmetadata element may be generated for each law firm that has tried acase in N.D.Cal. In some embodiments, the metadata elements areassembled into sets (e.g., judges) and subsets (e.g., judges at theN.D.Cal.) that can be used to formulate search parameters, At block 612,the legal analytics platform can identify portions of legal data thatinclude at least one legal entity. For example, a portion of legal data(e.g., an electronic legal document) may include references to a judge,a jurisdiction, a law firm, etc.

At block 614, the legal analytics platform can associate each portionwith at least one of the metadata elements, thereby identifying whatlegal entities are present. A portion may be associated with multiplemetadata elements in some instances. The multiple elements can be of thesame kind (e.g., multiple legal entities) or of different kinds (e.g., alegal entity and a legal event). At block 616, the legal analyticsplatform can construct a database that includes some or all of the legaldata and is searchable using the metadata elements (i.e., by legalentity). In some embodiments, a user is able to specify one or moreelements that are used to search the metadata in the database. Forexample, the user may designate combinations such as judge/judge,jurisdiction/judge, judge/law firm, etc. One skilled in the art willrecognize that any combination of legal entities could be used to searchthe database. At block 618, the legal analytics platform can generate aGUI that allows a user to interact with the platform, search thedatabase of legal data, and review search results, as will be discussedbelow with respect to FIG. 7.

FIG. 7 is a flow diagram depicting various steps in a process 700 forgenerating and implementing a GUI according to various embodiments. Atblock 702, the legal analytics platform (e.g., via GUI module 410 ofFIG. 4) generates a GUI. In some embodiments, the GUI is interactive andis configured to update in real-time in response to a user input. TheGUI can be configured to be presented by a web application or web-basedportal, web browser, or a mobile application adapted for a wearabledevice, cellular device, PDA, tablet, personal computer, etc. Moreover,the GUI could be presented using more than one layout. For example, afull version/layout for web browsers, tablets, personal computers, etc.,and a mobile version/layout for cellular devices, PDAs, etc.

At block 704, the GUI can allow a user to specify search parameters withwhich to search the database. The search parameters include one or moremetadata elements that are used to search the metadata and identifyrelevant legal data. As described above, in some instances the user mayspecify combinations of metadata elements (e.g., judge/jurisdiction) tofurther clarify what legal data is relevant and narrow the search. Whenspecifying the search parameters, the user may be able to selectpre-existing metadata elements (e.g., from a list) or manually input(e.g., type) data used to match or look up corresponding metadataelements (e.g., a party's name). While “searching” and “searchparameters” may be used throughout the Detailed Description, the systemsand processes described herein apply equally to browsing a legaldatabase. That is, a user may identify relevant documents or legalinformation by browsing through a legal database organized by category(e.g., narrowing from “Courts” to “N.D.Cal.” to “Patent Cases”).

At block 706, the GUI can display a search result to the user. Thesearch result may include legal data, selectable hyperlinks to legaldata, a textual summary, a graphical summary (e.g., a chart), etc. Forexample, the search result may include a textual summary 904 and agraphical summary 902, as shown in FIG. 9, for a portion or subset oflegal data specified by the user. At block 708, the GUI can allow theuser to filter the search result by modifying the search parameters. Auser can modify the search parameters by adding or removing metadataelements. The metadata elements can be designated as inclusive orexclusive when searching or filtering. In other words, a user candesignate an element (e.g., N.D.Cal.) as being included in the searchparameters (i.e., search result includes legal data tagged as“N.D.Cal.”) or excluded from the search parameters (i.e., search resultdoes not include legal data tagged as “N.D.Cal.,” even if the legal datamatches other search terms). The user's ability to include and excludemetadata elements affords significant flexibility in determiningappropriate search parameters. For example, if a case search included“N.D.Cal,” “C.D.Cal.” and “S.D.Cal.,” and excluded “Patent Cases,” thesearch result would include all cases in those jurisdictions that arenot tagged as patent cases.

FIG. 8 is a flow diagram depicting various steps in an entitynormalization and curation process 800 as may occur in some embodiments.At block 802, the legal analytics platform may receive an authoritativeversion of a legal entity name from an administrator that is known to becorrect. At block 804, legal analytics platform may store theauthoritative legal entity name (e.g., in storage 434 of FIG. 4). Oncestored, the authoritative legal entity name can be treated as the groundtruth value by a normalization module (e.g., normalization module 416 ofFIG. 4) that determines whether variants of the authoritative legalentity name exist in the legal data. However, as described above, theauthoritative legal entity name may not be provided in some embodiments.At block 806, the legal analytics platform identifies a variant of theauthoritative legal entity name in a portion of the legal data. Thevariant may be a typo, a misspelling, an outdated or alternative entityname, etc. Once the variant is identified, the portion of legal dataincluding the variant can be isolated for further processing. In thoseembodiments in which an authoritative version of the legal entity nameis not provided, the legal analytics platform can identify similarentity names and combine those similar entity names into a singlecategory. At block 808, the legal analytics platform remedies themistake by associating the portion with the authoritative legal entityname or single category rather than the variant. The normalizationprocess 800 ensures legal data (e.g., documents, outcomes) is correctlyattributed to the proper legal entity, rather than a variant. In someembodiments, a machine learning module (e.g., learning module 412 ofFIG. 4) is able to improve future normalization by updating the groundtruth value or storing common variants upon receiving an affirmationfrom a user or an administrator that a previous variant characterizationwas correct.

Although FIGS. 7 and 8 are largely described in the context of legalentities, these concepts could also be employed in systems and processesfor recognizing and identifying legal events, chronology and time, etc.

FIGS. 9, 10, and 11 are screenshots of GUIs 900, 1000, 1100 inaccordance with various embodiments. As illustrated by FIG. 9, the GUI900 can include both textual and graphical features. For example, GUI900 displays a summary page for a particular legal entity selected by auser. The GUI 900 includes a textual summary 904 of variants identifiedduring the normalization process and a graphical summary 902 of legaldata associated with the particular legal entity. In the presentembodiment, the graphical summary 902 includes a line chart showing casedistribution by case type (e.g., patent, trademark, copyright,antitrust) over time.

As illustrated by FIG. 10, a GUI 1000 can present results of variousstatistical analyses of legal data. GUI 1000, which presents a summarypage for “N.D.Cal.,” as selected by a user, includes a line chart 1002showing distribution by case type over time, a bar chart 1004 showingdistribution by judge, and a stacked column chart 1006 showingdistribution by case status. However, any form of textual or graphicalrepresentation could be used, including tables, lists, histograms, barcharts, pie charts, line charts, doughnut charts, bubble charts, etc.Analytical processes can be applied to any set or subset of the legaldata. For example, statistical analysis could be performed on legal dataassociated with a jurisdiction, judge, combination of judges, etc. GUI1000 also includes a filter button 1008 that allows the user to modifywhat legal data is analyzed.

When the user selects the filter button 1008, a filter menu 1102 mayappear as shown in FIG. 11. The filter menu 1102 allows the user tomodify what legal data is analyzed. Generally, a user will the filtermenu 1102 to specify or modify search parameters by adding or removingmetadata elements used to search the legal data. Search parameters mayinclude any combination of legal entities (e.g., courts, judges,parties), legal events (e.g., resolution, date of filing, termination,trial, damages), etc. As described above, the search parameters candesignate elements as being inclusive or exclusive when searching orfiltering. Some example elements are listed in Table 1, although thislist is not exhaustive. Other elements are also possible, such as asubgroup of elements for different procedural case resolutions, expertwitnesses, technology industry codes, legal industry codes (e.g., NiceAgreement international trademark classes, USPTO art classes),non-practicing entities, etc.

TABLE 1 Example Metadata Elements Legal Data Subsets Sample MetadataElement Options Case Status All; Open; Terminated Case Types All;Patent; Trademark; Copyright; Antitrust Case Tags All; ANDA; DeclaratoryJudgment; Appeal; Claim Construction Hearing; Trial; Jury Trial; BenchTrial Filed On Selectable range of dates Terminated Selectable range ofdates Trial Date Selectable range of dates Last Docket Selectable rangeof dates Date Courts All U.S. district courts Judges All U.S. districtjudges Case All; Likely Settlement; Procedural; Claimant Win; ClaimResolutions Defendant Win Case Damages All; Costs; Attorneys' Fees;Other/Mixed Damage Types; Statutory Damages - Willful (Copyright);Statutory Damages (Copyright); Statutory Damages - Willful (Trademark);Statutory Damages (Trademark); Prejudgment Interest; Reasonable Royalty;Trademark Owners' Actual Damages; Infringer's Profits; Actual Damages &Infringer's Profits; Lost Profits; Corrective Advertising; PublicPerformance License (§504d) Damages Selectable range of dates. AwardedDate Patent Findings All; Infringement; Non-Infringement; Invalidity; NoInvalidity; Unenforceability; No Unenforceability Patent All; 101Subject Matter; 102 Anticipation/Novelty; Invalidity 102(f) Derivation(pre-AIA); 102(g) Interference (pre- Reasons AIA); 103 Obviousness; 112Written Description; 112 Enablement; 112 Definiteness; 112 Best Mode(pre- AIA); 171 Improper Design Patent; 133, 371 Applica- tionAbandonment; 132, 251, 255, 305 Defective Correction; Obviousness-TypeDouble Patenting; No Invalidity Reason Specified Remedies PermanentInjunction; Preliminary Injunction; Seizure of Goods; TemporaryRestraining Order; Relinquish Domain Name; Termination of MarkLegal Analytics Based on Legal Event

FIG. 12 is a flow diagram depicting general steps in a legal analyticsprocess 1200 as may occur in some embodiments. The process 1200 foranalyzing legal events may be largely similar to process 600 of FIG. 6for analyzing legal entities. At block 1202, a legal analytics platformaccesses one or more sources of legal information, such as PACER, PAIR,or EDIS. At block 1204, the legal analytics platform can retrieve legaldata from at least one of the sources.

In some embodiments, the legal analytics platform is configured toidentify and cure inaccuracies in the legal data, shown at block 1206,and/or allow an administrator to modify the legal data and inputsupplementary legal data, shown at block 1208. Various embodiments mayinclude both, one, or neither of these steps. Further details regardingthe normalization process are described above with respect to FIG. 8.

At block 1210, the legal analytics platform can generate metadataelements that represent various legal events present in the legal data.Together, the various metadata elements constitute metadata that isassociated with the legal data. The legal events may include, forexample, case resolutions (e.g., likely settlement, procedural, claimantwin), damages types (e.g., compensatory lump, attorneys' fees), amountsof damages, patent findings (e.g., infringement, invalidity), etc. Insome embodiments, subgroups of elements are generated. For example, asubgroup of elements under “Procedural” may include “InterdistrictTransfer,” “Stay,” “Consolidation,” and “Dismissal.” The subgroups allowa user to identify a more specific segment of legal data on whichanalytics are to be applied. As another example, a subgroup of elementsunder “Invalidity” may include “101 Subject Matter,” “102Anticipation/Novelty,” and “103 Obviousness.” At block 1212, the legalanalytics platform can identify portions of legal data that include atleast one legal event.

At block 1214, the legal analytics platform can associate eachidentified portion with at least one element, thereby identifying whatlegal event is present. An identified portion may be associated withmultiple elements in some instances (e.g., a damage type and a damageamount, infringement and no invalidity). At block 1216, the legalanalytics platform can construct a database that includes some or all ofthe legal data and the metadata, and is searchable by metadata element.For example, the user may designate a single metadata element, such asInvalidity, or combinations of elements, such asInvalidity/Non-Infringement or Compensatory Lump Damages/Attorneys'Fees. Any combination of legal events may be used to search thedatabase. As described above, the elements can be designated asinclusive or exclusive when searching or filtering. At block 1218, thelegal analytics platform can generate a GUI that allows a user tointeract with the platform, search the database of legal data, reviewresults of statistical analysis, etc.

FIGS. 13, 14, and 15 are screenshots of GUIs 1300, 1400, 1500 inaccordance with various embodiments. As illustrated by FIG. 13, the GUI1300 can include a table 1302 and a chart 1304 that summarize anystatistical analyses performed on the relevant legal data. For example,both table 1302 and chart 1304 show case distribution by resolution typefor a legal entity (“N.D.Cal.”) selected by a user. In some embodiments,hovering over or selecting a segment of the chart causes an entry in thetable corresponding to the segment to become highlighted (e.g., row isoutlined, bolded, intensified).

A GUI 1400 may present search results or results of statistical analysisusing one or more tables. For example, GUI 1400 of FIG. 14 includestabular summaries of patent findings 1402 and patent invalidity reasons1404. The tabular summaries can be generated based on the metadataelements generated by process 1200 of FIG. 12 for legal outcomes. A GUI1500 may also present search results or results of statistical analysisusing charts or a combination of tables and charts. For example, GUI1500 of FIG. 15 includes a table 1502 and a chart 1504 that summarizedamage type and damage amount for the segment of legal data on whichanalytics were applied. In this case, analytics were applied to allintellectual property cases in N.D.Cal. between 2000-01-01 and2015-03-07. Chart 1504 also subdivides the segment of legal data byyear.

Legal Analytics Based on Timing

FIG. 16 is a flow diagram depicting general steps in a legal analyticsprocess 1600 as may occur in some embodiments. The process 1600 may belargely similar to process 600 of FIG. 6 for analyzing legal entities orprocess 1200 of FIG. 12 for analyzing legal events. At block 1602, alegal analytics platform accesses one or more sources of legalinformation, and at block 1604, the legal analytics platform retrieveslegal data from at least one of the sources. At block 1606, the legalanalytics platform can identify timestamps in the legal data. A“timestamp” is any sequence of characters or encoded information thatidentifies when a certain legal event occurred. Generally, the timestampincludes a date and time, although the legal data may be orderedchronologically in some embodiments.

The legal analytics platform can then associate each timestamp with aparticular legal event. At block 1608, the legal analytics platform cangenerate metadata elements that represent various legal events, such astermination of a case, commencement of a trial, commencement of a claimconstruction hearing, etc. In some embodiments, a user is able tospecify a particular legal event (e.g., issuance of declaratoryjudgment, filing of notice of appeal) for which a metadata element isgenerated. At block 1610, the legal analytics platform can tag eachtimestamp, thereby associating each timestamp with at least one metadataelement (i.e., at least one legal event).

At block 1612, the legal analytics engine can group the timestamps bylegal event. Each group represents a subset of timestamps that areassociated with a specific legal event. For example, a subset oftimestamps may be associated with commencement of claim constructionhearings. At block 1614, the legal analytics platform (e.g., viaanalytics engine 404) can perform statistical analysis using at leastone group/subset of timestamps. The statistical analysis may includedetermining the mean, median, minimum, maximum, etc., values for thegroup/subset. At block 1616, the legal analytics platform can generate aGUI that allows a user to interface with the platform, specify searchparameters, view search results, etc. At block 1618, the GUI can displaythe results of the statistical analysis and allow the user to select ormodify the group(s)/subset(s) of timestamps on which statisticalanalysis is performed.

FIG. 17 is a screenshot of a GUI 1700 displaying a summary of astatistical analysis of groups of timestamps as may occur in someembodiments. The summary of statistical measurements can presented usinga box plot 1702, although other charts may also be used such as barcharts, pie charts, line charts, doughnut charts, bubble charts, etc.The box plot 1702 can feature various measurements, including the median1704, a box 1706 that extends from a lower quartile to an upperquartile, and whiskers 1708 that extend from the box to a minimum value1710 and/or a maximum value 1712. In some embodiments, the minimum value1710 and the maximum value 1712 are determined after excluding outliers1714, which lie outside of the box 1706 by a distance of more than 1.5times the width of the box (i.e., difference between upper quartile andlower quartile).

The box plot 1702 may also include labels 1716 for some or all of themeasurements featured by the plot. For example, FIG. 17 includes labels1716 for the minimum value 1710, lower quartile, median value 1704,upper quartile, and maximum value 1712. In some embodiments, the boxplot 1702 includes an interactive control element 1718, which may alsobe referred to as a “slider,” that can be positioned within the box plot1718 by a user. Placement of the control element 1718 identifies aninput time that is used to determine a lower ratio 1720 and an upperratio 1722. The lower ratio 1720 represents the percentage of timestampsin a group/subset that fall below the input time, while the upper ratio1722 represents the percentage of timestamps in the group/subset thatexceed the input time. If the user moves the control element 1718 andchanges the input time, the lower ratio 1720 and upper ratio 1722 areupdated. The ratios may be updated in real-time. The measurements ofFIG. 17 can also be presented as part of a textual summary (e.g., atable) rather than, or in addition to, a graphical summary. In someembodiments, the GUI 1700 allows the user to modify the appearance ofthe box plot 1702. For example, the user may elect to conceal thecontrol element 1718, the labels 1716, the outliers 1714, etc.

In some embodiments it may be desirable to have the legal dataassociated with some combination of legal entities, legal events, etc.That is, the database may be searchable by any combination of theaforementioned metadata elements. For example, GUI 1500 of FIG. 15illustrates a segment of legal data that was determined by a user whofirst chose a legal entity (“N.D.Cal.”) and then chose a legal event(e.g., damages type, damages amount). Similarly, GUI 1700 of FIG. 17illustrates a segment of legal data that was determined by a user whofirst chose a legal entity (“N.D.Cal.”) and then elected to view asummary of timestamps grouped by legal event.

Legal Document Mapping

FIG. 18 is a flow diagram illustrating a process 1800 for retrieving,preparing, and delivering legal data according to various embodiments.Similar to those embodiments described above, process 1800 includes alegal analytics platform accessing one or more sources of legalinformation and retrieving legal data from at least one of the sources,as shown at blocks 1802 and 1804. The legal data can include electroniclegal documents, as well as case information, docket information,intellectual property information, etc.

At block 1806, the legal analytics platform (e.g., via an OCR module)can perform word recognition on the electronic documents, and, at block1808, the platform can establish a document type for each of theelectronic documents based on any recognized words. The document typemay be a motion, order (e.g., regarding injunction, dismissal, stay,consolidation, transfer, limine, etc.), pleading, complaint,counterclaim, corporate disclosure statement, answer, appeal, judgment,opinion, trial, claim construction order, final judgment, jury verdict,finding of fact and conclusion of law, etc. The legal analytics platformmay also be configured to identify electronic documents as an unknowntype (e.g., platform is unable to readily identify the document as aknown document type). “Unknown” documents could then be flagged formanual (i.e., human) review. At block 1810, each legal document can beassociated with one or more legal entities. For example, a legaldocument may be associated with a particular jurisdiction, judge, andlaw firm.

At block 1812, the legal analytics platform can construct a databasethat includes some or all of the electronic documents and is searchableby document type. The database may be the same database as thosedescribed by processes 600, 1200, or 1600 of FIGS. 6, 12, and 18,respectively. That is, the database may be searchable by any combinationof document type, legal entity, legal event, timestamp, etc. At block1814, the legal analytics platform (e.g., via GUI module 410 of FIG. 4)can generate a GUI that allows a user to interact with the legalanalytics platform and search the database. At block 1816, the GUIallows the user to specify search parameters, which may include somecombination metadata elements referring to document type, legal entity,etc.

In some embodiments, the legal analytics platform constructs sets oflegal documents that, together, create a “motion chain.” This process,illustrated by block 1818, may also be called motion mapping. Each setcan include a motion and any electronic documents that affect themotion's outcome, such as a response to the motion, a reply to theresponse, or an order that determines the outcome of the motion. The setmay also indicate what legal entities created the original document(s)(e.g., party, law firm) and/or the legal entities involved in ruling onthe motion (e.g., judge, jurisdiction). A set can also include anoutcome summary that indicates whether the motion was ultimately grantedor denied. Motion mapping can make it easier for a user to identifyuseful electronic documents in the legal data.

Sets of legal documents could be created for other legal events as well.For example, if a patent is asserted during litigation, the user maywant key documents related to the prosecution history (e.g.,application, office actions, responses) associated with the assertedpatent.

FIG. 19 is a screenshot of a GUI 1900 presenting legal documents as mayoccur in some embodiments. The GUI 1900 may be configured to presentsome portion or all of a legal document, a selectable hyperlink to thelegal document, or a document entry 1902. Each document entry 1902 caninclude one or more selectable hyperlinks 1904, an indicator 1906 thatidentifies document type, an outline 1908, or metadata elements 1910 forlegal entity, legal event, timestamp, etc. As described above, theelectronic documents stored in the database may be searchable by anycombination of metadata elements, thereby allowing the user to quicklyfind useful legal documents.

FIG. 20 is a screenshot of a GUI 2000 presenting a set of legaldocuments 2002 as may occur in some embodiments. As described above, thelegal analytics platform can be configured to construct sets of legaldocuments to form “motion chains.” Each set can include a motion and anylegal documents that affect the motion's outcome, such as responses,replies, or orders. For example, the set of legal documents 2002presented by GUI 2000 includes a motion 2004, various subsequentdocuments 2006 a-c, an outcome summary 2008, and a duration measurement2010. The outcome summary 2008 indicates whether the motion 2004 wasultimately granted or denied, while the duration measurement 2010calculates the total time from filing of the motion 2004 to issuance ofthe order 2006 c. The duration measurement 2010 may be calculated using,for example, the timestamps described above with respect to FIGS. 16-17.

Computer System

FIG. 21 is a block diagram illustrating an example of a computing system2100 in which at least some operations described herein can beimplemented. The computing system may include one or more centralprocessing units (“processors”) 2102, main memory 2106, non-volatilememory 2110, network adapter 2112 (e.g., network interfaces), videodisplay 2118, input/output devices 2120, control device 2122 (e.g.,keyboard and pointing devices), drive unit 2124 including a storagemedium 2126, and signal generation device 2130 that are communicativelyconnected to a bus 2116. The bus 2116 is illustrated as an abstractionthat represents any one or more separate physical buses, point to pointconnections, or both connected by appropriate bridges, adapters, orcontrollers. The bus 2116, therefore, can include, for example, a systembus, a Peripheral Component Interconnect (PCI) bus or PCI-Express bus, aHyperTransport or industry standard architecture (ISA) bus, a smallcomputer system interface (SCSI) bus, a universal serial bus (USB), IIC(I2C) bus, or an Institute of Electrical and Electronics Engineers(IEEE) standard 1394 bus, also called “Firewire.”

In various embodiments, the computing system 2100 operates as astandalone device, although the computing system 2100 may be connected(e.g., wired or wirelessly) to other machines. In a networkeddeployment, the computing system 2100 may operate in the capacity of aserver or a client machine in a client-server network environment, or asa peer machine in a peer-to-peer (or distributed) network environment.

The computing system 2100 may be a server computer, a client computer, apersonal computer, a user device, a tablet, a laptop computer, a PDA, acellular telephone, an iPhone, an iPad, a Blackberry, a processor, atelephone, a web appliance, a network router, switch or bridge, aconsole, a hand-held console, any portable/mobile hand-held device, orany machine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by the computing system2100.

While the main memory 2106, non-volatile memory 2110, and storage medium2126 (also called a “machine-readable medium”) are shown to be a singlemedium, the term “machine-readable medium” and “storage medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store one or more sets of instructions 2128. The term“machine-readable medium” and “storage medium” shall also be taken toinclude any medium that is capable of storing, encoding, or carrying aset of instructions for execution by the computing system and that causethe computing system to perform any one or more of the methodologies ofthe presently disclosed embodiments.

In general, the routines executed to implement the embodiments of thedisclosure, may be implemented as part of an operating system or aspecific application, component, program, object, module or sequence ofinstructions referred to as “computer programs.” The computer programstypically comprise one or more instructions (e.g., instructions 2104,2108, 2128) set at various times in various memory and storage devicesin a computer, and that, when read and executed by one or moreprocessing units or processors 2102, cause the computing system 2100 toperform operations to execute elements involving the various aspects ofthe disclosure.

Moreover, while embodiments have been described in the context of fullyfunctioning computers and computer systems, those skilled in the artwill appreciate that the various embodiments are capable of beingdistributed as a program product in a variety of forms, and that thedisclosure applies equally regardless of the particular type of machineor computer-readable media used to actually effect the distribution.

Further examples of machine-readable storage media, machine-readablemedia, or computer-readable (storage) media include, but are not limitedto, recordable type media such as volatile and non-volatile memorydevices 2110, floppy and other removable disks, hard disk drives,optical disks (e.g., Compact Disk Read-Only Memory (CD ROMS), DigitalVersatile Disks, (DVDs)), and transmission type media such as digitaland analog communication links.

The network adapter 2112 enables the computing system 2100 to mediatedata in a network 2114 with an entity that is external to the computingdevice 2100, through any known and/or convenient communications protocolsupported by the computing system 2100 and the external entity. Thenetwork adapter 2112 can include one or more of a network adaptor card,a wireless network interface card, a router, an access point, a wirelessrouter, a switch, a multilayer switch, a protocol converter, a gateway,a bridge, bridge router, a hub, a digital media receiver, and/or arepeater.

The network adapter 2112 can include a firewall that can, in someembodiments, govern and/or manage permission to access/proxy data in acomputer network, and track varying levels of trust between differentmachines and/or applications. The firewall can be any number of moduleshaving any combination of hardware and/or software components able toenforce a predetermined set of access rights between a particular set ofmachines and applications, machines and machines, and/or applicationsand applications, for example, to regulate the flow of traffic andresource sharing between these varying entities. The firewall mayadditionally manage and/or have access to an access control list whichdetails permissions including for example, the access and operationrights of an object by an individual, a machine, and/or an application,and the circumstances under which the permission rights stand.

Other network security functions can be performed or included in thefunctions of the firewall, can include, but are not limited to,intrusion-prevention, intrusion detection, next-generation firewall,personal firewall, etc.

As indicated above, the techniques introduced here implemented by, forexample, programmable circuitry (e.g., one or more microprocessors),programmed with software and/or firmware, entirely in special-purposehardwired (i.e., non-programmable) circuitry, or in a combination orsuch forms. Special-purpose circuitry can be in the form of, forexample, one or more application-specific integrated circuits (ASICs),programmable logic devices (PLDs), field-programmable gate arrays(FPGAs), etc.

Remarks

The foregoing description of various embodiments of the claimed subjectmatter has been provided for the purposes of illustration anddescription. It is not intended to be exhaustive or to limit the claimedsubject matter to the precise forms disclosed. Many modifications andvariations will be apparent to one skilled in the art. Embodiments werechosen and described in order to best describe the principles of theinvention and its practical applications, thereby enabling othersskilled in the relevant art to understand the claimed subject matter,the various embodiments, and the various modifications that are suitedto the particular uses contemplated.

While embodiments have been described in the context of fullyfunctioning computers and computer systems, those skilled in the artwill appreciate that the various embodiments are capable of beingdistributed as a program product in a variety of forms, and that thedisclosure applies equally regardless of the particular type of machineor computer-readable media used to actually effect the distribution.

Although the above Detailed Description describes certain embodimentsand the best mode contemplated, no matter how detailed the above appearsin text, the embodiments can be practiced in many ways. Details of thesystems and methods may vary considerably in their implementationdetails, while still being encompassed by the specification. As notedabove, particular terminology used when describing certain features oraspects of various embodiments should not be taken to imply that theterminology is being redefined herein to be restricted to any specificcharacteristics, features, or aspects of the invention with which thatterminology is associated. In general, the terms used in the followingclaims should not be construed to limit the invention to the specificembodiments disclosed in the specification, unless those terms areexplicitly defined herein. Accordingly, the actual scope of theinvention encompasses not only the disclosed embodiments, but also allequivalent ways of practicing or implementing the embodiments under theclaims.

The language used in the specification has been principally selected forreadability and instructional purposes, and it may not have beenselected to delineate or circumscribe the inventive subject matter. Itis therefore intended that the scope of the invention be limited not bythis Detailed Description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of variousembodiments is intended to be illustrative, but not limiting, of thescope of the embodiments, which is set forth in the following claims.

What is claimed is:
 1. A method for processing legal data and applyingtime-based analytics, the method comprising: accessing a source of legalinformation; retrieving legal data from the source of legal information,the legal data including case file documents and docket information;identifying a plurality of timestamps within the case file documents,the docket information, or both, wherein each timestamp of the pluralityof timestamps represents the occurrence of a legal event; generating afirst plurality of metadata elements, wherein each element correspondsto one of a plurality of legal events; tagging at least some of theplurality of timestamps with one of the first plurality of metadataelements, thereby associating each of the tagged timestamps with aparticular legal event; constructing a first plurality of subsets oftimestamps by grouping the plurality of timestamps by legal event;providing at least one subset to an analytics engine; performing astatistical analysis of the at least one subset; and presenting, by agraphical user interface, a result of the statistical analysis, whereinthe result includes a box plot that graphically depicts statisticalvariation of the at least one subset, wherein the box plot includes aninteractive control element that allows a user to specify an input timeand causes an upper ratio and a lower ratio to be displayed, wherein theupper ratio is a percentage of the at least one subset that exceeds theinput time and the lower ratio is a percentage of the at least onesubset that falls below the input time.
 2. The method of claim 1,further comprising: generating a second plurality of metadata elements,wherein each element corresponds to one of a plurality of legalentities; tagging at least some of the plurality of timestamps with oneof the second plurality of metadata elements, thereby associating eachof the tagged timestamps with a particular legal entity; andconstructing a second plurality of subsets of timestamps by grouping theplurality of timestamps by legal entity.
 3. The method of claim 2,further comprising: allowing a user to specify one or more legal events,legal entities, or both that are used to specify the at least one subseton which the statistical analysis is performed.
 4. The method of claim1, wherein the box plot includes a median value mark and a box thatextends from a lower quartile to an upper quartile.
 5. The method ofclaim 4, wherein the box plot includes whiskers marking a minimum valueand a maximum value that are determined after excluding outliers.
 6. Themethod of claim 1, wherein the source of legal information is PublicAccess to Court Electronic Records (PACER).
 7. A legal analyticsplatform comprising: a data crawler configured to access a source oflegal information and retrieve legal data from the source of legalinformation, the legal data including case file documents and docketinformation; a key milestone module configured to: identify a pluralityof timestamps within the case file documents, the docket information, orboth, wherein each timestamp represents the occurrence of a legal event;generate a plurality of metadata elements, wherein each metadata elementcorresponds to one of a plurality of legal events; and associate each ofthe plurality of timestamps with one of the plurality of metadataelements; and a legal analytics engine configured to: construct adatabase that includes the legal data and is searchable by legal event;generate a graphical user interface that allows a user to interact withthe legal analytics platform; construct subsets of timestamps bygrouping the plurality of timestamps by legal event, wherein a pluralityof subsets each comprise a plurality of timestamps; perform astatistical analysis on at least one of the subsets; and present, viathe graphical user interface, a result of the statistical analysis,wherein the result includes a box plot that graphically depictsstatistical variation of the at least one subset, wherein the box plotincludes an interactive control element that allows the user to specifyan input time and causes an upper ratio and a lower ratio to bedisplayed, wherein the upper ratio is a percentage of the at least onesubset that exceeds the input time and the lower ratio is a percentageof the at least one subset that falls below the input time.
 8. The legalanalytics plat-form of claim 7, wherein the subsets of timestamps aregrouped using the metadata elements associated with the each of theplurality of timestamps.
 9. The legal analytics platform of claim 8,wherein the graphical user interface is configured to: allow the user tospecify at least one legal event that is used to identify the at leastone subset on which the statistical analysis is performed.
 10. The legalanalytics platform of claim 7, wherein the legal event is termination ofa case, commencement of a trial, decision of an issue by judicial order,or commencement of a claim construction hearing.
 11. The legal analyticsplatform of claim 7, wherein the result includes a box plot thatgraphically depicts statistical variation of the at least one subset,the box plot including a median value mark, a box that extends from alower quartile to an upper quartile, and whiskers marking a minimumvalue and a maximum value that are determined after excluding outliers.12. A method for applying legal analytics, the method comprising:accessing a source of legal information; retrieving legal data from thesource of legal information; identifying a plurality of timestamps inthe legal data, wherein each timestamp represents an occurrence of alegal event; associating each timestamp with one of a plurality ofmetadata elements, wherein the metadata elements correspond to distinctlegal events; constructing a database that includes the legal data andis searchable by legal event; constructing one or more subsets oftimestamps from the plurality of timestamps, the one or more subsetsformed by grouping the plurality of timestamps by legal event; providingat least one subset to an analytics engine; performing a statisticalanalysis of the at least one subset; and displaying, by a graphical userinterface, a result of the statistical analysis, wherein the resultincludes a box plot that graphically depicts statistical variation ofthe at least one subset, wherein the box plot includes an interactiveslider that allows a user to modify the result by specifying an inputtime and causing an upper ratio and a lower ratio to be displayed,wherein the upper ratio is a percentage of the at least one subset thatexceeds the input time and the lower ratio is a percentage of the atleast one subset that falls below the input time.
 13. The method ofclaim 12, wherein the legal data includes case file documents, docketinformation, or both.
 14. The method of claim 12, wherein the resultincludes a textual summary and a chart.
 15. The method of claim 14,wherein the textual summary includes a mean value, a median value, alower quartile value, an upper quartile value, a minimum value, amaximum value, a standard deviation value, or any combination thereof.16. The method of claim 15, wherein the chart includes one or morelabels that represent values included in the textual summary.
 17. Themethod of claim 14, further comprising: generating a graphical userinterface configured to: allow a user to specify search parameters withwhich to search the database; display the result of the statisticalanalysis; allow the user to modify the result by adjusting the searchparameters; and allow the user to interact with the chart through theuse of a control element.
 18. The method of claim 12, wherein thedistinct legal events include termination of a case, commencement of atrial, commencement of a claim construction hearing, or any combinationthereof.
 19. The method of claim 12, wherein the source of legalinformation is Public Access to Court Electronic Records (PACER), PatentApplication Information Retrieval (PAIR), or Electronic DocumentInformation System (EDIS).
 20. A method for applying time-based legalanalytics, the method comprising: accessing, via an interactivegraphical user interface, a legal analytics platform configured to:access a source of legal information; retrieve legal data from thesource of legal information; identify a plurality of timestamps in thelegal data that represent occurrences of legal events; associating atleast some of the plurality of timestamps with a first metadata elementof a first plurality of metadata elements, the first plurality ofmetadata elements corresponding to distinct legal events; associating atleast some of the plurality of timestamps with a second tag of a secondplurality of metadata elements, the second plurality of metadataelements corresponding to distinct legal entities; and construct adatabase that includes the legal data and is searchable by legal eventand legal entity; specifying search parameters that are used to searchthe database, the search parameters identifying one or more legalevents, legal entities, or both; causing the legal analytics platform toidentify a subset of timestamps, comprising a plurality of timestamps,from the plurality of timestamps that satisfy the search parameters;causing the legal analytics platform to apply legal analytics to thesubset of timestamps; and reviewing, via the interactive graphical userinterface, analytic results that include a graphical summary of thesubset of timestamps, wherein the analytic results include a box plotthat graphically depicts statistical variation of the subset, whereinthe box plot includes an interactive control element that allows a userto specify an input time and causes an upper ratio and a lower ratio tobe displayed, wherein the upper ratio is a percentage of the subset thatexceeds the input time and the lower ratio is a percentage of the subsetthat falls below the input time.